Critical success factors of big data projects: A model proposal and empirical test

The explosion of data being captured and stored in information systems has created a new area of challenges and opportunities for information technology (IT) professionals. While substantial efforts have been made towards algorithms and technologies that are used to perform these analytics, comparatively there has been limited empirical research on Critical Success Factors (CSFs) that relate to Big Data projects. The lack of critical success factor sources can doom an IS project to a certain failure. This research promises to help organizations to identify factors that impact success – as perceived by practitioners and professionals – on Big Data projects. The main purpose of this research is to build on the current diverse literature around Big Data by contributing discussion and data that allow common agreement on factors that influence successful Big Data projects. The research also validates the CSF scale and theoretical CSF model statistically. While individual and technical factors have been explored as they relate to Big Data success, there is a gap in the literature in determining the critical factors in the light of the views of Big Data experts. Even though critical success factors have been discussed previously as being related to IS success, it has not been associated with Big Data project success. The most complete information regarding the CSFs for Big Data projects can be received from Big Data professionals within those departments that have been involved in Big Data projects. Accordingly, this study is conducted with 17 Big Data experts in earlier Delphi Study and 827 Big Data professionals in large scale survey administration. At the end of the study, five CSFs emerged in addition to a statistically reliable and valid CSF measurement scale and a relational research model that is tested and validated. This research is exploratory in nature. The best approach for such a study was mixed methods utilizing Constructivist Grounded Theory. Grounded theory allows the researcher to begin with the question, collect data, examine ideas and concepts, extract and categorize that data to use it to form the basis of a new theory. This new theory can then be applied and tested statistically. To successfully accomplish this, the approach for the study was fragmented into a three-part mixed methods study. A qualitative section utilizing semi-structured interviews and Delphi study with experts in the field followed by a quantitative section to test relationships between core concepts derived from the qualitative section.